From d8dc3c7bd74fe03d4b50181ec6f045ceb677a0d0 Mon Sep 17 00:00:00 2001
From: The-ML-Hero <67855775+The-ML-Hero@users.noreply.github.com>
Date: Fri, 29 Jan 2021 13:52:51 +0530
Subject: [PATCH 1/9] Create sidebar.py

---
 templates/Style Transfer_Pytorch/sidebar.py | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 templates/Style Transfer_Pytorch/sidebar.py

diff --git a/templates/Style Transfer_Pytorch/sidebar.py b/templates/Style Transfer_Pytorch/sidebar.py
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/templates/Style Transfer_Pytorch/sidebar.py	
@@ -0,0 +1 @@
+

From f6f69bd1a318c6b8b47fcdc2ad677fa5a619c7f9 Mon Sep 17 00:00:00 2001
From: The-ML-Hero <67855775+The-ML-Hero@users.noreply.github.com>
Date: Fri, 29 Jan 2021 14:05:42 +0530
Subject: [PATCH 2/9] Update sidebar.py

---
 templates/Style Transfer_Pytorch/sidebar.py | 92 +++++++++++++++++++++
 1 file changed, 92 insertions(+)

diff --git a/templates/Style Transfer_Pytorch/sidebar.py b/templates/Style Transfer_Pytorch/sidebar.py
index 8b13789..fa56e43 100644
--- a/templates/Style Transfer_Pytorch/sidebar.py	
+++ b/templates/Style Transfer_Pytorch/sidebar.py	
@@ -1 +1,93 @@
+import streamlit as st
 
+MODELS = {
+     "VGG": {
+        "VGG11": "vgg11",
+        "VGG11 with batch normalization": "vgg11_bn",
+        "VGG13": "vgg13",
+        "VGG13 with batch normalization": "vgg13_bn",
+        "VGG16": "vgg16",
+        "VGG16 with batch normalization": "vgg16_bn",
+        "VGG19": "vgg19",
+        "VGG19 with batch normalization": "vgg19_bn",
+    },
+}
+
+
+# Define possible optimizers in a dict.
+# Format: optimizer -> default learning rate
+OPTIMIZERS = {
+    "Adam": 0.001,
+    "Adadelta": 1.0,
+    "Adagrad": 0.01,
+    "Adamax": 0.002,
+    "RMSprop": 0.01,
+    "SGD": 0.1,
+}
+
+
+
+def show():
+    """Shows the sidebar components for the template and returns user inputs as dict."""
+    
+    # `show()` is the only method required in this module. You can add any other code 
+    # you like above or below. 
+    
+    inputs = {}  # dict to store all user inputs until return
+    
+    with st.sidebar:
+        
+        # Render all template-specific sidebar components here. 
+
+        # Use ## to denote sections. Common sections for training templates: 
+        # Model, Input data, Preprocessing, Training, Visualizations
+        st.write("## Model")
+        
+        # Store all user inputs in the `inputs` dict. This will be passed to the code
+        # template later.
+        inputs["model"] = st.selectbox("Which model?", list(MODELS.keys()))
+        
+        
+        inputs["pretrained"] = st.checkbox("Use pre-trained model (Suggested Use is with a pretrained model)")
+        
+        st.write("## Input data")
+        inputs["data_format"] = st.selectbox(
+            "Which data do you want to use?",
+            ("Public Image File", "Custom Image files"),
+        )
+        
+        if input["data_format"]== "Public Image File":
+          st.write("""
+          ```
+          It's a public image so you won't have to do anything.
+          ```
+          """)
+        
+        elif input["data_format"]== "Custom Image File":
+          st.write("""
+          ```
+          Make sure you have two files the content image and the sytle image.
+          ```
+          """) 
+        
+        inputs["loss"] = st.selectbox(
+            "Loss function", ("Mean Squared Error Loss")
+        )
+        
+        inputs["optimizer"] = st.selectbox("Optimizer", list(OPTIMIZERS.keys()))
+        
+        default_lr = OPTIMIZERS[inputs["optimizer"]]
+        inputs["lr"] = st.number_input(
+            "Learning rate", 0.000, None, default_lr, format="%f"
+        )
+        
+        inputs["num_epochs"] = st.number_input("Epochs", 1, None, 5)
+               
+        
+    return inputs
+
+
+# To test the sidebar independent of the app or template, just run 
+# `streamlit run sidebar.py` from within this folder.
+if __name__ == "__main__":
+    show()

From caf7050893b5c99a1892cef6ffe3139360adc231 Mon Sep 17 00:00:00 2001
From: The-ML-Hero <67855775+The-ML-Hero@users.noreply.github.com>
Date: Fri, 29 Jan 2021 14:08:59 +0530
Subject: [PATCH 3/9] Create code-template.py.jinja

---
 .../code-template.py.jinja                    | 336 ++++++++++++++++++
 1 file changed, 336 insertions(+)
 create mode 100644 templates/Style Transfer_Pytorch/code-template.py.jinja

diff --git a/templates/Style Transfer_Pytorch/code-template.py.jinja b/templates/Style Transfer_Pytorch/code-template.py.jinja
new file mode 100644
index 0000000..2b7366a
--- /dev/null
+++ b/templates/Style Transfer_Pytorch/code-template.py.jinja	
@@ -0,0 +1,336 @@
+# Before running, install required packages:
+{% if notebook %}
+
+!
+{%- else %}
+#
+{%- endif %}
+ pip install numpy torch torchvision pytorch-ignite{% if visualization_tool == "Tensorboard" %} tensorboardX tensorboard{% endif %}{% if visualization_tool == "Weights & Biases" %} wandb{% endif %}{% if visualization_tool == "comet.ml" %} comet_ml{% endif %}{% if visualization_tool == "Aim" %} aim{% endif %}
+{% if visualization_tool == "Weights & Biases" %}
+
+
+# Also, you need to login to Weights & Biases on the terminal:
+{% if notebook %}
+
+! wandb login
+{% else %}
+# wandb login
+{% endif %}
+{% endif %}
+{% if notebook %}
+
+
+# ---
+{% endif %}
+
+
+{% if visualization_tool == "comet.ml" %}
+from comet_ml import Experiment  # has to be 1st import
+{% endif %}
+import numpy as np
+import torch
+from torch import optim, nn
+from torch.utils.data import DataLoader, TensorDataset
+from torchvision import models, datasets, transforms
+from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
+from ignite.metrics import Accuracy, Loss
+{% if data_format == "Image files" %}
+import urllib
+import zipfile
+{% endif %}
+{% if visualization_tool == "Tensorboard" or checkpoint %}
+from datetime import datetime
+{% endif %}
+{% if visualization_tool == "Tensorboard" %}
+from tensorboardX import SummaryWriter
+{% elif visualization_tool == "Aim" %}
+from aim import Session
+{% elif visualization_tool == "Weights & Biases" %}
+import wandb
+{% endif %}
+{% if checkpoint %}
+from pathlib import Path
+{% endif %}
+
+{% if data_format == "Numpy arrays" %}
+def fake_data():
+    # 4 images of shape 1x16x16 with labels 0, 1, 2, 3
+    return [np.random.rand(4, 1, 16, 16), np.arange(4)]
+
+{% elif data_format == "Image files" %}
+# COMMENT THIS OUT IF YOU USE YOUR OWN DATA.
+# Download example data into ./data/image-data (4 image files, 2 for "dog", 2 for "cat").
+url = "https://github.com/jrieke/traingenerator/raw/main/data/fake-image-data.zip"
+zip_path, _ = urllib.request.urlretrieve(url)
+with zipfile.ZipFile(zip_path, "r") as f:
+    f.extractall("data")
+
+{% endif %}
+
+{{ header("Setup") }}
+{% if data_format == "Numpy arrays" %}
+# INSERT YOUR DATA HERE
+# Expected format: [images, labels]
+# - images has array shape (num samples, color channels, height, width)
+# - labels has array shape (num samples, )
+train_data = fake_data()  # required
+val_data = fake_data()    # optional
+test_data = None          # optional
+{% elif data_format == "Image files" %}
+# INSERT YOUR DATA HERE
+# Expected format: One folder per class, e.g.
+# train
+# --- dogs
+# |   +-- lassie.jpg
+# |   +-- komissar-rex.png
+# --- cats
+# |   +-- garfield.png
+# |   +-- smelly-cat.png
+#
+# Example: https://github.com/jrieke/traingenerator/tree/main/data/image-data
+train_data = "data/image-data"  # required
+val_data = "data/image-data"    # optional
+test_data = None                # optional
+{% elif data_format == "Public dataset"%}
+# Dataset {{ dataset }} will be loaded further down.
+{% endif %}
+
+# Set up hyperparameters.
+lr = {{ lr }}
+batch_size = {{ batch_size }}
+num_epochs = {{ num_epochs }}
+
+# Set up logging.
+{% if visualization_tool == "Tensorboard" or checkpoint %}
+experiment_id = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
+{% endif %}
+{% if visualization_tool == "Tensorboard" %}
+writer = SummaryWriter(logdir=f"logs/{experiment_id}")
+{% elif visualization_tool == "Aim" %}
+aim_session = Session({% if aim_experiment %}experiment="{{ aim_experiment }}"{% endif %})
+aim_session.set_params({"lr": lr, "batch_size": batch_size, "num_epochs": num_epochs}, name="hparams")
+{% elif visualization_tool == "Weights & Biases" %}
+wandb.init(
+{% if wb_project %}
+    project="{{ wb_project }}", 
+{% endif %}
+{% if wb_name %}
+    name="{{ wb_name }}", 
+{% endif %}
+    config={"lr": lr, "batch_size": batch_size, "num_epochs": num_epochs}
+)
+{% elif visualization_tool == "comet.ml" %}
+experiment = Experiment("{{ comet_api_key }}"{% if comet_project %}, project_name="{{ comet_project }}"{% endif %})
+{% endif %}
+{% if checkpoint %}
+checkpoint_dir = Path(f"checkpoints/{experiment_id}")
+checkpoint_dir.mkdir(parents=True, exist_ok=True)
+{% endif %}
+print_every = {{ print_every }}  # batches
+
+# Set up device.
+{% if gpu %}
+use_cuda = torch.cuda.is_available()
+{% else %}
+use_cuda = False
+{% endif %}
+device = torch.device("cuda" if use_cuda else "cpu")
+
+
+{% if data_format == "Public dataset" %}
+{{ header("Dataset & Preprocessing") }}
+def load_data(train):
+    # Download and transform dataset.
+    transform = transforms.Compose([
+        transforms.Resize(256), 
+        transforms.CenterCrop(224), 
+        transforms.ToTensor(), 
+        {% if dataset == "MNIST" or dataset == "FashionMNIST"%}
+        transforms.Lambda(lambda x: x.repeat(3, 1, 1)),  # grayscale to RGB
+        {% endif %}
+    ])
+    dataset = datasets.{{ dataset }}("./data", train=train, download=True, transform=transform)
+
+    # Wrap in data loader.
+    if use_cuda:
+        kwargs = {"pin_memory": True, "num_workers": 1}
+    else:
+        kwargs = {}
+    loader = DataLoader(dataset, batch_size=batch_size, shuffle=train, **kwargs)
+    return loader
+
+train_loader = load_data(train=True)
+val_loader = None
+test_loader = load_data(train=False)
+{% else %}
+{{ header("Preprocessing") }}
+def preprocess(data, name):
+    if data is None:  # val/test can be empty
+        return None
+
+    {% if data_format == "Image files" %}
+    # Read image files to pytorch dataset.
+    transform = transforms.Compose([
+        transforms.Resize(256), 
+        transforms.CenterCrop(224), 
+        transforms.ToTensor(), 
+        {# TODO: Maybe add normalization option even if model is not pretrained #}
+        {% if pretrained %}
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        {% endif %}
+    ])
+    dataset = datasets.ImageFolder(data, transform=transform)
+    {% elif data_format == "Numpy arrays" %}
+    images, labels = data
+
+    # Rescale images to 0-255 and convert to uint8.
+    # Note: This is done for each dataset individually, which is usually ok if all 
+    # datasets look similar. If not, scale all datasets based on min/ptp of train set.
+    images = (images - np.min(images)) / np.ptp(images) * 255
+    images = images.astype(np.uint8)
+
+    # If images are grayscale, convert to RGB by duplicating channels.
+    if images.shape[1] == 1:
+        images = np.stack((images[:, 0],) * 3, axis=1)
+
+    # Resize images and transform images torch tensor.
+    images = images.transpose((0, 2, 3, 1))  # channels-last, required for transforms.ToPILImage
+    transform = transforms.Compose([
+        transforms.ToPILImage(),
+        transforms.Resize(256), 
+        transforms.CenterCrop(224), 
+        transforms.ToTensor(), 
+        {% if pretrained %}
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+        {% endif %}
+    ])
+    {# TODO: This is quite ugly and very inefficient #}
+    images = torch.stack(list(map(transform, images)))
+
+    # Convert labels to tensors.
+    labels = torch.from_numpy(labels).long()
+
+    # Construct dataset.
+    dataset = TensorDataset(images, labels)
+    {% endif %}
+
+    # Wrap in data loader.
+    {% if gpu %}
+    if use_cuda:
+        kwargs = {"pin_memory": True, "num_workers": 1}
+    else:
+        kwargs = {}
+    loader = DataLoader(dataset, batch_size=batch_size, shuffle=(name=="train"), **kwargs)
+    {% else %}
+    loader = DataLoader(dataset, batch_size=batch_size, shuffle=(name=="train"))
+    {% endif %}
+    return loader
+
+train_loader = preprocess(train_data, "train")
+val_loader = preprocess(val_data, "val")
+test_loader = preprocess(test_data, "test")
+{% endif %}
+
+
+{{ header("Model") }}
+# Set up model, loss, optimizer.
+model = models.{{ model_func }}(pretrained={{ pretrained }})
+{# TODO: Maybe enable this by default, so that people can adapt num_classes afterward. #}
+{% if num_classes != 1000 %}
+num_classes = {{ num_classes }}
+{% if "resnet" in model_func %}
+model.fc = torch.nn.Linear(in_features=model.fc.in_features, out_features=num_classes, bias=True)
+{% elif "alexnet" in model_func or "vgg" in model_func %}
+model.classifier[-1] = torch.nn.Linear(in_features=model.classifier[-1].in_features, out_features=num_classes, bias=True)
+{% elif "densenet" in model_func %}
+model.classifier = torch.nn.Linear(in_features=model.classifier.in_features, out_features=num_classes, bias=True)
+{% endif %}
+{% endif %}
+model = model.to(device)
+loss_func = nn.{{ loss }}()
+optimizer = optim.{{ optimizer }}(model.parameters(), lr=lr)
+
+{% if visualization_tool == "Weights & Biases" %}
+# Log gradients and model parameters to W&B.
+wandb.watch(model)
+
+{% endif %}
+
+{{ header("Training") }}
+# Set up pytorch-ignite trainer and evaluator.
+trainer = create_supervised_trainer(
+    model,
+    optimizer,
+    loss_func,
+    device=device,
+)
+{# TODO: Atm, the train metrics get accumulated, see torch_models.py #}
+metrics = {
+    "accuracy": Accuracy(),
+    "loss": Loss(loss_func),
+}
+evaluator = create_supervised_evaluator(
+    model, metrics=metrics, device=device
+)
+
+@trainer.on(Events.ITERATION_COMPLETED(every=print_every))
+def log_batch(trainer):
+    batch = (trainer.state.iteration - 1) % trainer.state.epoch_length + 1
+    print(
+        f"Epoch {trainer.state.epoch} / {num_epochs}, "
+        f"batch {batch} / {trainer.state.epoch_length}: "
+        f"loss: {trainer.state.output:.3f}"
+    )
+
+@trainer.on(Events.EPOCH_COMPLETED)
+def log_epoch(trainer):
+    print(f"Epoch {trainer.state.epoch} / {num_epochs} average results: ")
+
+    def log_results(name, metrics, epoch):
+        print(
+            f"{name + ':':6} loss: {metrics['loss']:.3f}, "
+            f"accuracy: {metrics['accuracy']:.3f}"
+        )
+        {% if visualization_tool == "Tensorboard" %}
+        writer.add_scalar(f"{name}_loss", metrics["loss"], epoch)
+        writer.add_scalar(f"{name}_accuracy", metrics["accuracy"], epoch)
+        {% elif visualization_tool == "Aim" %}
+        aim_session.track(metrics["loss"], name="loss", subset=name, epoch=epoch)
+        aim_session.track(metrics["accuracy"], name="accuracy", subset=name, epoch=epoch)
+        {% elif visualization_tool == "Weights & Biases" %}
+        wandb.log({f"{name}_loss": metrics["loss"], f"{name}_accuracy": metrics["accuracy"]})
+        {% elif visualization_tool == "comet.ml" %}
+        experiment.log_metric(f"{name}_loss", metrics["loss"])
+        experiment.log_metric(f"{name}_accuracy", metrics["accuracy"])
+        {% endif %}
+
+    # Train data.
+    evaluator.run(train_loader)
+    log_results("train", evaluator.state.metrics, trainer.state.epoch)
+    
+    # Val data.
+    if val_loader:
+        evaluator.run(val_loader)
+        log_results("val", evaluator.state.metrics, trainer.state.epoch)
+
+    # Test data.
+    if test_loader:
+        evaluator.run(test_loader)
+        log_results("test", evaluator.state.metrics, trainer.state.epoch)
+
+    print()
+    print("-" * 80)
+    print()
+
+{# TODO: Maybe use this instead: https://pytorch.org/ignite/handlers.html#ignite.handlers.ModelCheckpoint #}
+{% if checkpoint %}
+@trainer.on(Events.EPOCH_COMPLETED)
+def checkpoint_model(trainer):
+    torch.save(model, checkpoint_dir / f"model-epoch{trainer.state.epoch}.pt")
+
+{% endif %}
+# Start training.
+trainer.run(train_loader, max_epochs=num_epochs)
+{% if visualization_tool == "Weights & Biases" %}
+wandb.finish()
+{% endif %}

From 9a7bfd79fda1a15d4fa6dae5183cd54a22612fb0 Mon Sep 17 00:00:00 2001
From: The-ML-Hero <67855775+The-ML-Hero@users.noreply.github.com>
Date: Fri, 29 Jan 2021 14:19:41 +0530
Subject: [PATCH 4/9] Update sidebar.py

---
 templates/Style Transfer_Pytorch/sidebar.py | 11 +----------
 1 file changed, 1 insertion(+), 10 deletions(-)

diff --git a/templates/Style Transfer_Pytorch/sidebar.py b/templates/Style Transfer_Pytorch/sidebar.py
index fa56e43..9d4ac08 100644
--- a/templates/Style Transfer_Pytorch/sidebar.py	
+++ b/templates/Style Transfer_Pytorch/sidebar.py	
@@ -1,16 +1,7 @@
 import streamlit as st
 
 MODELS = {
-     "VGG": {
-        "VGG11": "vgg11",
-        "VGG11 with batch normalization": "vgg11_bn",
-        "VGG13": "vgg13",
-        "VGG13 with batch normalization": "vgg13_bn",
-        "VGG16": "vgg16",
-        "VGG16 with batch normalization": "vgg16_bn",
-        "VGG19": "vgg19",
-        "VGG19 with batch normalization": "vgg19_bn",
-    },
+     "VGG": "VGG19": "vgg19",
 }
 
 

From 73f5a19461a690598648827fb61f33c4c203e4f2 Mon Sep 17 00:00:00 2001
From: The-ML-Hero <67855775+The-ML-Hero@users.noreply.github.com>
Date: Fri, 29 Jan 2021 14:21:15 +0530
Subject: [PATCH 5/9] Update sidebar.py

---
 templates/Style Transfer_Pytorch/sidebar.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/templates/Style Transfer_Pytorch/sidebar.py b/templates/Style Transfer_Pytorch/sidebar.py
index 9d4ac08..19b98a0 100644
--- a/templates/Style Transfer_Pytorch/sidebar.py	
+++ b/templates/Style Transfer_Pytorch/sidebar.py	
@@ -38,7 +38,11 @@ def show():
         # template later.
         inputs["model"] = st.selectbox("Which model?", list(MODELS.keys()))
         
-        
+        inputs["Device"] = st.selectbox(
+            "Which device would you like to train on?",
+            ("GPU", "CPU"),
+        )
+     
         inputs["pretrained"] = st.checkbox("Use pre-trained model (Suggested Use is with a pretrained model)")
         
         st.write("## Input data")

From f2342b502ee293214f06261518a5c5e0d9bdee5c Mon Sep 17 00:00:00 2001
From: The-ML-Hero <67855775+The-ML-Hero@users.noreply.github.com>
Date: Fri, 29 Jan 2021 14:45:09 +0530
Subject: [PATCH 6/9] Update code-template.py.jinja

---
 .../code-template.py.jinja                    | 379 +++---------------
 1 file changed, 62 insertions(+), 317 deletions(-)

diff --git a/templates/Style Transfer_Pytorch/code-template.py.jinja b/templates/Style Transfer_Pytorch/code-template.py.jinja
index 2b7366a..06ff47c 100644
--- a/templates/Style Transfer_Pytorch/code-template.py.jinja	
+++ b/templates/Style Transfer_Pytorch/code-template.py.jinja	
@@ -1,336 +1,81 @@
-# Before running, install required packages:
-{% if notebook %}
-
-!
-{%- else %}
-#
-{%- endif %}
- pip install numpy torch torchvision pytorch-ignite{% if visualization_tool == "Tensorboard" %} tensorboardX tensorboard{% endif %}{% if visualization_tool == "Weights & Biases" %} wandb{% endif %}{% if visualization_tool == "comet.ml" %} comet_ml{% endif %}{% if visualization_tool == "Aim" %} aim{% endif %}
-{% if visualization_tool == "Weights & Biases" %}
-
-
-# Also, you need to login to Weights & Biases on the terminal:
-{% if notebook %}
-
-! wandb login
-{% else %}
-# wandb login
-{% endif %}
-{% endif %}
-{% if notebook %}
-
-
-# ---
-{% endif %}
-
-
-{% if visualization_tool == "comet.ml" %}
-from comet_ml import Experiment  # has to be 1st import
-{% endif %}
-import numpy as np
 import torch
-from torch import optim, nn
-from torch.utils.data import DataLoader, TensorDataset
-from torchvision import models, datasets, transforms
-from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
-from ignite.metrics import Accuracy, Loss
-{% if data_format == "Image files" %}
-import urllib
-import zipfile
-{% endif %}
-{% if visualization_tool == "Tensorboard" or checkpoint %}
-from datetime import datetime
-{% endif %}
-{% if visualization_tool == "Tensorboard" %}
-from tensorboardX import SummaryWriter
-{% elif visualization_tool == "Aim" %}
-from aim import Session
-{% elif visualization_tool == "Weights & Biases" %}
-import wandb
-{% endif %}
-{% if checkpoint %}
-from pathlib import Path
-{% endif %}
-
-{% if data_format == "Numpy arrays" %}
-def fake_data():
-    # 4 images of shape 1x16x16 with labels 0, 1, 2, 3
-    return [np.random.rand(4, 1, 16, 16), np.arange(4)]
-
-{% elif data_format == "Image files" %}
-# COMMENT THIS OUT IF YOU USE YOUR OWN DATA.
-# Download example data into ./data/image-data (4 image files, 2 for "dog", 2 for "cat").
-url = "https://github.com/jrieke/traingenerator/raw/main/data/fake-image-data.zip"
-zip_path, _ = urllib.request.urlretrieve(url)
-with zipfile.ZipFile(zip_path, "r") as f:
-    f.extractall("data")
-
-{% endif %}
-
-{{ header("Setup") }}
-{% if data_format == "Numpy arrays" %}
-# INSERT YOUR DATA HERE
-# Expected format: [images, labels]
-# - images has array shape (num samples, color channels, height, width)
-# - labels has array shape (num samples, )
-train_data = fake_data()  # required
-val_data = fake_data()    # optional
-test_data = None          # optional
-{% elif data_format == "Image files" %}
-# INSERT YOUR DATA HERE
-# Expected format: One folder per class, e.g.
-# train
-# --- dogs
-# |   +-- lassie.jpg
-# |   +-- komissar-rex.png
-# --- cats
-# |   +-- garfield.png
-# |   +-- smelly-cat.png
-#
-# Example: https://github.com/jrieke/traingenerator/tree/main/data/image-data
-train_data = "data/image-data"  # required
-val_data = "data/image-data"    # optional
-test_data = None                # optional
-{% elif data_format == "Public dataset"%}
-# Dataset {{ dataset }} will be loaded further down.
-{% endif %}
-
-# Set up hyperparameters.
-lr = {{ lr }}
-batch_size = {{ batch_size }}
-num_epochs = {{ num_epochs }}
-
-# Set up logging.
-{% if visualization_tool == "Tensorboard" or checkpoint %}
-experiment_id = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
-{% endif %}
-{% if visualization_tool == "Tensorboard" %}
-writer = SummaryWriter(logdir=f"logs/{experiment_id}")
-{% elif visualization_tool == "Aim" %}
-aim_session = Session({% if aim_experiment %}experiment="{{ aim_experiment }}"{% endif %})
-aim_session.set_params({"lr": lr, "batch_size": batch_size, "num_epochs": num_epochs}, name="hparams")
-{% elif visualization_tool == "Weights & Biases" %}
-wandb.init(
-{% if wb_project %}
-    project="{{ wb_project }}", 
-{% endif %}
-{% if wb_name %}
-    name="{{ wb_name }}", 
-{% endif %}
-    config={"lr": lr, "batch_size": batch_size, "num_epochs": num_epochs}
-)
-{% elif visualization_tool == "comet.ml" %}
-experiment = Experiment("{{ comet_api_key }}"{% if comet_project %}, project_name="{{ comet_project }}"{% endif %})
-{% endif %}
-{% if checkpoint %}
-checkpoint_dir = Path(f"checkpoints/{experiment_id}")
-checkpoint_dir.mkdir(parents=True, exist_ok=True)
-{% endif %}
-print_every = {{ print_every }}  # batches
-
-# Set up device.
-{% if gpu %}
-use_cuda = torch.cuda.is_available()
-{% else %}
-use_cuda = False
-{% endif %}
-device = torch.device("cuda" if use_cuda else "cpu")
-
-
-{% if data_format == "Public dataset" %}
-{{ header("Dataset & Preprocessing") }}
-def load_data(train):
-    # Download and transform dataset.
-    transform = transforms.Compose([
-        transforms.Resize(256), 
-        transforms.CenterCrop(224), 
-        transforms.ToTensor(), 
-        {% if dataset == "MNIST" or dataset == "FashionMNIST"%}
-        transforms.Lambda(lambda x: x.repeat(3, 1, 1)),  # grayscale to RGB
-        {% endif %}
-    ])
-    dataset = datasets.{{ dataset }}("./data", train=train, download=True, transform=transform)
-
-    # Wrap in data loader.
-    if use_cuda:
-        kwargs = {"pin_memory": True, "num_workers": 1}
-    else:
-        kwargs = {}
-    loader = DataLoader(dataset, batch_size=batch_size, shuffle=train, **kwargs)
-    return loader
-
-train_loader = load_data(train=True)
-val_loader = None
-test_loader = load_data(train=False)
-{% else %}
-{{ header("Preprocessing") }}
-def preprocess(data, name):
-    if data is None:  # val/test can be empty
-        return None
-
-    {% if data_format == "Image files" %}
-    # Read image files to pytorch dataset.
-    transform = transforms.Compose([
-        transforms.Resize(256), 
-        transforms.CenterCrop(224), 
-        transforms.ToTensor(), 
-        {# TODO: Maybe add normalization option even if model is not pretrained #}
-        {% if pretrained %}
-        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-        {% endif %}
-    ])
-    dataset = datasets.ImageFolder(data, transform=transform)
-    {% elif data_format == "Numpy arrays" %}
-    images, labels = data
-
-    # Rescale images to 0-255 and convert to uint8.
-    # Note: This is done for each dataset individually, which is usually ok if all 
-    # datasets look similar. If not, scale all datasets based on min/ptp of train set.
-    images = (images - np.min(images)) / np.ptp(images) * 255
-    images = images.astype(np.uint8)
-
-    # If images are grayscale, convert to RGB by duplicating channels.
-    if images.shape[1] == 1:
-        images = np.stack((images[:, 0],) * 3, axis=1)
+from torch import nn 
+from torch import optim
+from torchvision import transforms
+from PIL import Image
+import torchvision.models as models
+from torchvision.utils import save_image
+import numpy as np
 
-    # Resize images and transform images torch tensor.
-    images = images.transpose((0, 2, 3, 1))  # channels-last, required for transforms.ToPILImage
-    transform = transforms.Compose([
-        transforms.ToPILImage(),
-        transforms.Resize(256), 
-        transforms.CenterCrop(224), 
-        transforms.ToTensor(), 
-        {% if pretrained %}
-        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-        {% endif %}
-    ])
-    {# TODO: This is quite ugly and very inefficient #}
-    images = torch.stack(list(map(transform, images)))
+device = {{Device}}
+model = models.vgg19(pretrained={{pretrained}}).features
+print(model)
 
-    # Convert labels to tensors.
-    labels = torch.from_numpy(labels).long()
+class VGG(nn.Module):
+  def __init__(self):
+    super(VGG,self).__init__()
+    self.choosen_features = ['0','5','10','19','28']
+    self.model = models.vgg19(pretrained=True).features[:29]
 
-    # Construct dataset.
-    dataset = TensorDataset(images, labels)
-    {% endif %}
+  def forward(self,x):
+    features = []
 
-    # Wrap in data loader.
-    {% if gpu %}
-    if use_cuda:
-        kwargs = {"pin_memory": True, "num_workers": 1}
-    else:
-        kwargs = {}
-    loader = DataLoader(dataset, batch_size=batch_size, shuffle=(name=="train"), **kwargs)
-    {% else %}
-    loader = DataLoader(dataset, batch_size=batch_size, shuffle=(name=="train"))
-    {% endif %}
-    return loader
+    for layer_num , layer in enumerate(self.model):
+      x = layer(x)
 
-train_loader = preprocess(train_data, "train")
-val_loader = preprocess(val_data, "val")
-test_loader = preprocess(test_data, "test")
-{% endif %}
+      if str(layer_num) in  self.choosen_features:
+        features.append(x)
 
+    return features    
 
-{{ header("Model") }}
-# Set up model, loss, optimizer.
-model = models.{{ model_func }}(pretrained={{ pretrained }})
-{# TODO: Maybe enable this by default, so that people can adapt num_classes afterward. #}
-{% if num_classes != 1000 %}
-num_classes = {{ num_classes }}
-{% if "resnet" in model_func %}
-model.fc = torch.nn.Linear(in_features=model.fc.in_features, out_features=num_classes, bias=True)
-{% elif "alexnet" in model_func or "vgg" in model_func %}
-model.classifier[-1] = torch.nn.Linear(in_features=model.classifier[-1].in_features, out_features=num_classes, bias=True)
-{% elif "densenet" in model_func %}
-model.classifier = torch.nn.Linear(in_features=model.classifier.in_features, out_features=num_classes, bias=True)
-{% endif %}
-{% endif %}
-model = model.to(device)
-loss_func = nn.{{ loss }}()
-optimizer = optim.{{ optimizer }}(model.parameters(), lr=lr)
+model = VGG().to(device).eval()
+loader = transforms.Compose([
+    transforms.Resize((540,540)) ,
+    transforms.ToTensor(),                        
+])
+def load_image(image_name):
+  img = Image.open(image_name)
+  img = loader(img).unsqueeze(0)
+  return img.to(device)
 
-{% if visualization_tool == "Weights & Biases" %}
-# Log gradients and model parameters to W&B.
-wandb.watch(model)
+image_size = 540
+original_img = load_image('./style.jpg')
+style_img = load_image('./style.jpg')
 
-{% endif %}
+generated = original_img.clone().requires_grad_(True)
 
-{{ header("Training") }}
-# Set up pytorch-ignite trainer and evaluator.
-trainer = create_supervised_trainer(
-    model,
-    optimizer,
-    loss_func,
-    device=device,
-)
-{# TODO: Atm, the train metrics get accumulated, see torch_models.py #}
-metrics = {
-    "accuracy": Accuracy(),
-    "loss": Loss(loss_func),
-}
-evaluator = create_supervised_evaluator(
-    model, metrics=metrics, device=device
-)
+total_steps = {{ epochs }}
+learning_rate = {{ lr }}
+alpha = 1 
+beta = 0.01
+optimizer = optim.{{optimizer}}([generated],lr=learning_rate)
 
-@trainer.on(Events.ITERATION_COMPLETED(every=print_every))
-def log_batch(trainer):
-    batch = (trainer.state.iteration - 1) % trainer.state.epoch_length + 1
-    print(
-        f"Epoch {trainer.state.epoch} / {num_epochs}, "
-        f"batch {batch} / {trainer.state.epoch_length}: "
-        f"loss: {trainer.state.output:.3f}"
-    )
+for step in range(total_steps):
+  generated_features = model(generated)
+  original_img_features = model(original_img)
+  style_img_features = model(style_img)
 
-@trainer.on(Events.EPOCH_COMPLETED)
-def log_epoch(trainer):
-    print(f"Epoch {trainer.state.epoch} / {num_epochs} average results: ")
+  style_loss = original_loss = 0
 
-    def log_results(name, metrics, epoch):
-        print(
-            f"{name + ':':6} loss: {metrics['loss']:.3f}, "
-            f"accuracy: {metrics['accuracy']:.3f}"
-        )
-        {% if visualization_tool == "Tensorboard" %}
-        writer.add_scalar(f"{name}_loss", metrics["loss"], epoch)
-        writer.add_scalar(f"{name}_accuracy", metrics["accuracy"], epoch)
-        {% elif visualization_tool == "Aim" %}
-        aim_session.track(metrics["loss"], name="loss", subset=name, epoch=epoch)
-        aim_session.track(metrics["accuracy"], name="accuracy", subset=name, epoch=epoch)
-        {% elif visualization_tool == "Weights & Biases" %}
-        wandb.log({f"{name}_loss": metrics["loss"], f"{name}_accuracy": metrics["accuracy"]})
-        {% elif visualization_tool == "comet.ml" %}
-        experiment.log_metric(f"{name}_loss", metrics["loss"])
-        experiment.log_metric(f"{name}_accuracy", metrics["accuracy"])
-        {% endif %}
-
-    # Train data.
-    evaluator.run(train_loader)
-    log_results("train", evaluator.state.metrics, trainer.state.epoch)
+  for gen_fea,ori_fea,style_fea in zip(generated_features,original_img_features,style_img_features):
+    batch_size , channel,height,width = gen_fea.shape
+    original_loss += torch.mean((gen_fea - ori_fea) ** 2)
     
-    # Val data.
-    if val_loader:
-        evaluator.run(val_loader)
-        log_results("val", evaluator.state.metrics, trainer.state.epoch)
+    G = gen_fea.view(channel,height*width).mm(
+        gen_fea.view(channel,height*width).t()
+    )
 
-    # Test data.
-    if test_loader:
-        evaluator.run(test_loader)
-        log_results("test", evaluator.state.metrics, trainer.state.epoch)
+    A = style_fea.view(channel,height*width).mm(
+        style_fea.view(channel,height*width).t()
+    )
 
-    print()
-    print("-" * 80)
-    print()
+    style_loss += torch.mean((G - A)**2)
 
-{# TODO: Maybe use this instead: https://pytorch.org/ignite/handlers.html#ignite.handlers.ModelCheckpoint #}
-{% if checkpoint %}
-@trainer.on(Events.EPOCH_COMPLETED)
-def checkpoint_model(trainer):
-    torch.save(model, checkpoint_dir / f"model-epoch{trainer.state.epoch}.pt")
+  total_loss = alpha*original_loss + beta * style_loss
+  optimizer.zero_grad()
+  total_loss.backward()
+  optimizer.step()
 
-{% endif %}
-# Start training.
-trainer.run(train_loader, max_epochs=num_epochs)
-{% if visualization_tool == "Weights & Biases" %}
-wandb.finish()
-{% endif %}
+  if step % {{save}} == 0:
+    print(f'Total_Loss: {total_loss}')
+    save_image(generated,'Generated.png')

From 8820d6ee66ad19b660510d3f4df5af7d5b68f62b Mon Sep 17 00:00:00 2001
From: The-ML-Hero <67855775+The-ML-Hero@users.noreply.github.com>
Date: Fri, 29 Jan 2021 14:47:38 +0530
Subject: [PATCH 7/9] Update sidebar.py

---
 templates/Style Transfer_Pytorch/sidebar.py | 20 +++++++-------------
 1 file changed, 7 insertions(+), 13 deletions(-)

diff --git a/templates/Style Transfer_Pytorch/sidebar.py b/templates/Style Transfer_Pytorch/sidebar.py
index 19b98a0..7a46a76 100644
--- a/templates/Style Transfer_Pytorch/sidebar.py	
+++ b/templates/Style Transfer_Pytorch/sidebar.py	
@@ -1,7 +1,7 @@
 import streamlit as st
 
 MODELS = {
-     "VGG": "VGG19": "vgg19",
+     "VGG": "vgg19",
 }
 
 
@@ -48,23 +48,17 @@ def show():
         st.write("## Input data")
         inputs["data_format"] = st.selectbox(
             "Which data do you want to use?",
-            ("Public Image File", "Custom Image files"),
+            ("Custom Image files"),
         )
         
-        if input["data_format"]== "Public Image File":
+        if input["data_format"]== "Custom Image files":
           st.write("""
           ```
-          It's a public image so you won't have to do anything.
+          Make sure you have style.jpg and content.jpg .
           ```
           """)
-        
-        elif input["data_format"]== "Custom Image File":
-          st.write("""
-          ```
-          Make sure you have two files the content image and the sytle image.
-          ```
-          """) 
-        
+          
+          
         inputs["loss"] = st.selectbox(
             "Loss function", ("Mean Squared Error Loss")
         )
@@ -76,7 +70,7 @@ def show():
             "Learning rate", 0.000, None, default_lr, format="%f"
         )
         
-        inputs["num_epochs"] = st.number_input("Epochs", 1, None, 5)
+        inputs["num_epochs"] = st.number_input("Epochs", 1, None, 5000)
                
         
     return inputs

From 242f7acb36dc7e9009aaa84a27020966fde841c1 Mon Sep 17 00:00:00 2001
From: The-ML-Hero <67855775+The-ML-Hero@users.noreply.github.com>
Date: Fri, 29 Jan 2021 14:49:04 +0530
Subject: [PATCH 8/9] Update code-template.py.jinja

---
 templates/Style Transfer_Pytorch/code-template.py.jinja | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/templates/Style Transfer_Pytorch/code-template.py.jinja b/templates/Style Transfer_Pytorch/code-template.py.jinja
index 06ff47c..a45c541 100644
--- a/templates/Style Transfer_Pytorch/code-template.py.jinja	
+++ b/templates/Style Transfer_Pytorch/code-template.py.jinja	
@@ -39,7 +39,7 @@ def load_image(image_name):
   return img.to(device)
 
 image_size = 540
-original_img = load_image('./style.jpg')
+original_img = load_image('./content.jpg')
 style_img = load_image('./style.jpg')
 
 generated = original_img.clone().requires_grad_(True)
@@ -76,6 +76,6 @@ for step in range(total_steps):
   total_loss.backward()
   optimizer.step()
 
-  if step % {{save}} == 0:
+  if step % {{ visualize_per_epoch }} == 0:
     print(f'Total_Loss: {total_loss}')
     save_image(generated,'Generated.png')

From 0411903c14d87cfccad63fd32a27ccb8ce9df684 Mon Sep 17 00:00:00 2001
From: The-ML-Hero <67855775+The-ML-Hero@users.noreply.github.com>
Date: Fri, 29 Jan 2021 14:49:23 +0530
Subject: [PATCH 9/9] Update sidebar.py

---
 templates/Style Transfer_Pytorch/sidebar.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/templates/Style Transfer_Pytorch/sidebar.py b/templates/Style Transfer_Pytorch/sidebar.py
index 7a46a76..674d559 100644
--- a/templates/Style Transfer_Pytorch/sidebar.py	
+++ b/templates/Style Transfer_Pytorch/sidebar.py	
@@ -60,7 +60,7 @@ def show():
           
           
         inputs["loss"] = st.selectbox(
-            "Loss function", ("Mean Squared Error Loss")
+            "Loss function", ("Style Gram Loss")
         )
         
         inputs["optimizer"] = st.selectbox("Optimizer", list(OPTIMIZERS.keys()))
@@ -71,6 +71,8 @@ def show():
         )
         
         inputs["num_epochs"] = st.number_input("Epochs", 1, None, 5000)
+          
+        inputs["visualize_per_epoch"] = st.number_input("Epochs", 1, None, 200)
                
         
     return inputs